Radiographic imaging is a familiar tool not only for medical and dental diagnostics, but also for security identification and non-destructive test (NDT) applications. In radiographic imaging a detector, placed behind the subject with respect to the radiation source, responds to directed radiation by forming an image that is representative of the relative absorption of incident X-rays by the subject. Detector types for X-ray imaging range from digital radiography (DR) devices that generate electronic image data directly, to computed radiography (CR) apparatus that employ a re-usable phosphor sheet or plate that is scanned following exposure, and to film X-ray systems that can be scanned and digitized to provide digital image data.
Speckle artifacts can occur in the X-ray image, particularly in industrial and test environments, due to various types of particulate in the imaging path. This can include dust or dirt, metal filings, or other contaminants on the X-ray plate itself or on or near other components in the imaging chain. Unless speckle is suppressed, the resulting displayed image can be difficult to interpret, compromising the usefulness of the radiographic image as a diagnostic testing tool.
Conventional approaches to the problem of correcting image speckle have not been satisfactory, for various reasons. The problem is complex, since speckle can occur anywhere in the image, over both highly absorptive and non-absorptive portions of the subject. This means that data values for speckle can vary within the same image, depending on location, and frustrates more straightforward approaches, such as applying global thresholding to identify and isolate speckle, for example. Statistical methods that detect dust or other particulate can be of some value; however, such methods inherently require multiple image captures from the same equipment and taken in the same environment, which may not be possible for some applications and can be costly and time-consuming, generating considerable data that is not used directly for imaging.
It is preferred that the method for threshold correction should not compromise the image content. Thus, there is a need for a speckle suppression method that selectively identifies speckle in each image and compensates for speckle artifacts without distorting the data content.